Sakata lingam-Estimation of data generating processes using SEM - Shohei SHIMIZU

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Structural equation models SEM are mathematical models that can be used to describe data generating processes.

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Structural equation models SEM are mathematical models that can be used to describe data generating processes. An important application of those methods is causal discovery. Non-Gaussianity and independence are the keys to model identification as in independent component analysis ICA. Peters and P. Identifiability of Gaussian structural equation models with equal error variances. Biometrika , 1 : , Entner and P. Estimating a causal order among groups of variables in linear models.

Hoyer, A. Scheines, P. Spirtes, J. Ramsey, G. Lacerda, and S. Causal discovery of linear acyclic models with arbitrary distributions. Shimizu, P. A linear non-Gaussian acyclic model for causal discovery. Journal of Machine Learning Research , 7 Oct : , Shimizu, A. Kano and P. Discovery of non-gaussian linear causal models using ICA. Dodge and V. On asymmetric properties of the correlation coefficient in the regression setting. The American Statistician , 55 1 : , Direction dependence in a regression line.

Communications in Statistics - Theory and Methods , 29 : , Zhang, S. Zhou, J. Guan, J. Zhou, C. Yan, J. Guan, X. Zheng, B. Aragam, P. Ravikumar, and E.

Yang, N. Li, N. An, Y. Chen, and G. An efficient causal structure learning algorithm for linear arbitrarily distributed continuous data. The Journal of Supercomputing , pp. Direction dependence analysis: A framework to test the direction of effects in linear models with an implementation in SPSS. Behavior Research Methods , pp. Cai, J. Qiao, Z. Zhang, and Z.

SELF: Structural equational likelihood framework for causal discovery. Cai, F. Xie, W. Chen, and Z. An efficient kurtosis-based causal discovery method for linear non-Gaussian acyclic data. Wiedermann, M. Hagmann and A. Significance tests to determine the direction of effects in linear regression models. British Journal of Mathematical and Statistical Psychology , 68 1 : , Feng, F. Chen and W. Learning linear non-Gaussian networks: A new view from matrix identification.

Pairwise likelihood ratios for estimation of non-Gaussian structural equation models. Journal of Machine Learning Research , 14 Jan : , Pairwise measures of causal direction in linear non-Gaussian acyclic models. On direction of dependence. Metrika , , Henao and O. Sparse linear identifiable multivariate modeling. Journal of Machine Learning Research , 12 Mar : , Bayesian sparse factor models and DAGs inference and comparison. Hoyer and A. Bayesian discovery of linear acyclic causal models.

In Proc. Shimizu, T. Inazumi, Y. Sogawa, A. Kawahara, T. Washio, P. Hoyer and K. Journal of Machine Learning Research , 12 Apr : , Inazumi, S. Shimizu and T. Use of prior knowledge in a non-Gaussian method for learning linear structural equation models. Sogawa, S. Shimizu, Y.

Kawahara and T. An experimental comparison of linear non-Gaussian causal discovery methods and their variants. A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model. Shimamura, A. Washio and S. Estimating exogenous variables in data with more variables than observations.

Washio, T. Shimamura and S. Discovery of exogenous variables in data with more variables than observations. Ozaki, K. Nakamura and H. A multilevel model using 2nd and 3rd order moments. Proceedings of the Institute of Statistical Mathematics , 58 2 : , Zhang, H.

Peng, L. Chan and A. ICA with sparse connections: Revisited. Nielsen and L.

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Raghu P. Use the link below to share a full-text version of this article with your friends and colleagues. Learn more. The aim of the current study was to gain an understanding of the experiences and aspirations of young people living with Developmental Coordination Disorder DCD in their own words.

Eleven young people aged 11—16 years with a prior diagnosis of DCD were identified from child health records of two participating NHS trusts. All interviews were recorded verbatim and transcribed. Narrative data were analysed using Lindseth's interpretive phenomenology. Subthemes illustrated the attitude of the young people to their day to day lives, their difficulties and strategies used by the young people to overcome these difficulties in school and at home. The attitude of the school to difference, the presence of bullying, the accepting nature of the class, teachers and peers were vitally important.

Areas of life that encouraged a positive sense of identity and worth included being part of a social network that gave the young people a sense of belonging, potentially one that valued differences as well as similarities.

The current work highlights the need for services to adopt a model of DCD where the young person talks about what they can do and considers strategies of overcoming their difficulties. This has implications for education and future intervention strategies that focus on fostering psychological resilience and educational coping strategies rather than simply attempting to improve motor skills.

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Abstract Aims The aim of the current study was to gain an understanding of the experiences and aspirations of young people living with Developmental Coordination Disorder DCD in their own words. Methods Eleven young people aged 11—16 years with a prior diagnosis of DCD were identified from child health records of two participating NHS trusts.

Conclusion The current work highlights the need for services to adopt a model of DCD where the young person talks about what they can do and considers strategies of overcoming their difficulties.

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